The healthcare sector has the most stable growth rate of any sector of the U.S. economy. Furthermore, the demand for healthcare services typically increases proportionally to the age of the population. Since an average individual over age 65 consumes four-times more healthcare dollars than an average individual under age 65, the growth rate of the healthcare sector is likely to increase because the percentage of the U.S. population over age 65 will increase from 12% in 1992 to 18% in 2020.
A data warehouse is a collection of data designed to support clinical as well as patient management decision making. A data warehouse typically contains a wide variety of data that present a coherent picture of clinical or business conditions at a single point in time. Development of a data warehouse includes development of systems to extract data from operating systems and installation of a warehouse database system that provides clinicians or managers flexible access to the data. The term “data warehousing” generally refers to combining many different databases across an entire enterprise. In contrast, a “data mart” is a database, or collection of databases, designed to help clinicians and managers identify therapeutic strategies or make strategic, clinical, and business decisions about their patients. Whereas a data warehouse combines databases across an entire enterprise, data marts are usually smaller and focus on a particular subject or department. Some data marts, called dependent data marts, are subsets of larger data warehouses.
The vast accumulation of medical information and technology is opening doors for the discovery of new diagnostics, disease prevention strategies, and drug and device therapies for a host of diseases, including, but not limited to, cancer, heart disease, diabetes, hypertension, mental illness, allergic reaction, immune disorder, and infectious disease. Many diseases correlate to other specific contributory factors including genetic factors, family history, dietary issues, geographical locations, demographic data, and environmental factors. Thus, there is great interest in identifying these contributory factors to improve the accuracy of disease diagnosis and treatment. Moreover, since the future of healthcare will focus on disease prevention as well as past treatment and diagnosis, an important objective will be to identify individuals at risk for developing a disease.
One of the most powerful medical advances in recent years has been the increase in genetic information available to researchers and clinicians. Genomic studies will result in the development of a plethora of targeted therapies because researchers and clinicians will soon have the ability to profile variations in the Deoxyribonucleic Acid (DNA) of an individual and predict responses to a particular medicine. From the physician's perspective, identifying that a patient is likely to have a genetically based reaction to a drug will be of paramount importance. Approximately 7% of all patients have severe adverse reactions to prescribed medications, with drug side effects being the 5th leading cause of death in the United States in 1997 (Pharmacogenomics-Offering a Wealth of Targets for the Pharma Prospector; IMS Health Web Site). Thus, a need exists for clinical intelligence to enable a physician to prospectively identify when a clinical profile, family history, or symptom for a patient suggests a genetically based reaction to a particular therapy. A patient identified in this manner will be a candidate for genetic screening to definitely determine whether they have the genetic anomaly that will cause an adverse side effect. A physician will be able to use this information to prescribe more effective medicines and treatments.
In addition to identifying therapeutic strategies, the healthcare industry recognizes that a database system containing electronic medical records (EMRs) would improve patient care and increase the operational efficiency of the physician's practice. An efficient EMR system would provide valuable information for a broad range of applications, including but not limited to, diagnostic, therapeutic, marketing research (i.e., passive recruitment of a research population), clinical trial recruitment, and marketing services (i.e., active recruitment of a research population). Even though EMR companies have developed EMR systems and marketed the benefit of the EMR for more than a decade, adoption of the technology has been slow because integration of those systems requires not only monetary cost, but also workflow modifications. Thus, automation in most physicians' practices is limited to small-scale client-server based billing and scheduling applications. Very few physician practices have EMR software or other database management capability, and fewer still have information technology (IT) support. Yet there is a growing need for EMR management because of the increasingly complex regulatory environment facing clinicians. Remaining compliant with new healthcare regulations and practice guidelines is nearly impossible with a paper-based system. Moreover,
PCT patent application serial number WO 00/51053 refers to a clinical and diagnostic database that contains patient records including phenotype, genotype, and sample information for the patient. The database system described in that PCT application, however, relies primarily upon genotype or stored sample information to generate correlations between phenotype and genotype.
Moreover, the medical database in the prior art force a physician to modify the normal process for collecting information because those databases rely on a physician to complete a questionnaire or involve other specific restrictions on data entry that are inconvenient and undesirable for the physician. Exemplary medical databases in the prior art include the epidemiological database disclosed in U.S. Pat. Ser. No. 5,911,132, and the MedLEE information extraction system disclosed in U.S. Pat. Ser. No. 6,182,029. Thus, there is a need for a database system that can generate information concerning either a disease risk or a susceptibility type, or drug response polymorphisms without requiring clinicians to change individual practice behavior.
A successful product or service in the healthcare industry will benefit the quality of life for a large number of patients by focusing on the physician's tasks and presenting a cost-effective solution to a recognized problem. A healthcare industry product and service that automates the collection and processing of clinical documentation by a physician will also provide clinical and economic value to the patient's medical record.
FIG. 1 illustrates the prior art clinical documentation process. The process begins when patient 100 visits physician 110 for a clinical reason. The visit can be in any clinical setting such as a private office, a health clinic, or a hospital and for any clinical reason such as an annual physical or to remedy of a specific medical ailment. As a result of the visit, physician 110 compiles a clinical note that may include historic medical information, vital signs, symptomatic descriptions, pharmaceutical prescriptions, or diagnostic conclusions. Following the visit, physician 110 connects to transcription service 130 using public switched telephone network (PSTN) 120 to dictate the clinical note for patient 100. Transcription service 130 stores the dictated clinical note in an audio format on storage device 131. Transcriptionist 132 retrieves the dictated clinical note from storage device 131, transcribes the note into electronic medical record 135, and stores electronic medical record 135 in a digital format on storage device 131. Physician 110 reviews electronic medical record 135 and stores a printed copy of electronic medical record 135 in paper based charting 140 associated with patient 100.
Following the visit with patient 100, physician 110 may recommend that clinical provider 115 perform a clinical test on patient 100. Physician 110 receives the results of the clinical test, reviews the results, discusses the results with patient 100, and stores the results in paper based charting 140 associated with patient 100.
The prior art clinical documentation process shown in FIG. 1 lacks the ability to efficiently search for data that is not known to be associated with a specific patient. Thus, there is a need for a system, method, and apparatus that automates the clinical documentation process and provides for storage and retrieval of clinical, diagnostic, and treatment data input in a natural human language format. The system, method, and apparatus will provide software tools to define disease or clinical term taxonomies that group the parsed data and define search criteria to enable intelligent searching of the data warehouse. The system, method, and apparatus disclosed herein automates the clinical documentation process and provides an engine and search tools for a data warehouse that unlocks the clinical and economic value of patient medical records.